Semi-Supervised Framework for Wafer Defect Pattern Recognition with Enhanced Labeling

L. Chen, Katherine Shu-Min Li, Xu-Hao Jiang, Sying-Jyan Wang, Andrew Yi-Ann Huang, Jwu E. Chen, Hsing-Chung Liang, Chung-Lung Hsu
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引用次数: 1

Abstract

Wafer map defect pattern recognition is valuable for root cause analysis and yield learning. Most of the previous studies on defect pattern recognition are based on supervised machine learning, in which labeled wafer maps are used to train a machine learning model for automatic classification. Some problems arise in this approach. First, there may be misclassification in the original labeled data, which makes it difficult to establish an accurate prediction model. Secondly, defect patterns that are not defined before will not be classified correctly. In this paper, we proposed a semi-supervised framework to deal with these problems. Labeled wafer maps are first used to train a prediction model, with likely misclassified data excluded. The prediction model is then used to classify unlabeled data. The remaining data that cannot be properly classified are then sent to an unsupervised learning algorithm to extract more defect patterns with enhanced labeling techniques. This proposed approach is validated with TSMC 811K database, in which we are able to define five new defect pattern types. Experimental results show that total 14 defect types can be recognized with overall accuracy of 94.37%.
基于增强标记的晶圆缺陷模式识别半监督框架
晶圆图缺陷模式识别对于根本原因分析和良率学习是有价值的。以往对缺陷模式识别的研究大多是基于监督式机器学习,利用标记的晶圆图来训练机器学习模型进行自动分类。这种方法产生了一些问题。首先,原始标注数据可能存在误分类,难以建立准确的预测模型。其次,之前没有定义的缺陷模式将不能被正确分类。在本文中,我们提出了一个半监督框架来处理这些问题。标记的晶圆图首先用于训练预测模型,排除可能的错误分类数据。然后使用预测模型对未标记的数据进行分类。剩下的不能被正确分类的数据被发送到一个无监督的学习算法,用增强的标记技术提取更多的缺陷模式。该方法在TSMC 811K数据库中得到验证,在该数据库中,我们能够定义五种新的缺陷模式类型。实验结果表明,共识别出14种缺陷类型,总体准确率为94.37%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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